{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/multi_modal/gpt4v_experiments_cot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GPT4-V Experiments with General, Specific questions and Chain Of Thought (COT) Prompting Technique."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GPT-4V has amazed us with its ability to analyze images and even generate website code from visuals.\n",
    "\n",
    "This tutorial notebook investigates GPT-4V's proficiency in interpreting bar charts, scatter plots, and tables. We aim to assess whether specific questioning and chain of thought prompting can yield better responses compared to broader inquiries. Our demonstration seeks to determine if GPT-4V can exceed these known limitations with precise questioning and systematic reasoning techniques.\n",
    "\n",
    "We observed in these experiments that asking specific questions, rather than general ones, yields better answers. Let's delve into these experiments."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: This tutorial notebook aims to inform the community about GPT-4V's performance, though the results might not be universally applicable. We strongly advise conducting tests with similar questions on your own dataset before drawing conclusions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have put to test following images from [Llama2](https://arxiv.org/pdf/2307.09288.pdf) and [MistralAI](https://arxiv.org/pdf/2310.06825.pdf) papers.\n",
    "\n",
    "1. Violation percentage of safety with different LLMs across categories. (Llama2 paper)\n",
    "2. Llama2 vs Mistral model performances across various NLP tasks.(Mistral paper)\n",
    "2. Performances of different LLMs across various NLP tasks. (Llama2 paper)\n",
    "\n",
    "Let's inspect each of these images now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start analyzing these images by following these steps for our questions:\n",
    "\n",
    "1. General Question: Simply ask, \"Analyze the image.\"\n",
    "2. Specific Inquiry: Question the performance of a certain category or model in detail.\n",
    "3. Chain of Thought Prompting: Use a step-by-step reasoning method to walk through the analysis.\n",
    "\n",
    "These guidelines aim to test how different questioning techniques might improve the precision of the information we gather from the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-multi-modal-llms-openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "OPENAI_API_KEY = \"YOUR OPENAI API KEY\"\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import SimpleDirectoryReader\n",
    "from llama_index.multi_modal_llms.openai import OpenAIMultiModal\n",
    "\n",
    "openai_mm_llm = OpenAIMultiModal(\n",
    "    model=\"gpt-4o\",\n",
    "    api_key=OPENAI_API_KEY,\n",
    "    max_new_tokens=500,\n",
    "    temperature=0.0,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/gpt4_experiments/llama2_mistral.png' -O './llama2_mistral.png'\n",
    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/gpt4_experiments/llama2_model_analysis.pdf' -O './llama2_model_analysis.png'\n",
    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/gpt4_experiments/llama2_violations_charts.png' -O './llama2_violations_charts.png'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image1 - Violation percentage of safety with different LLMs across categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x785d08fb6800>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = Image.open(\"llama2_violations_charts.png\")\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# put your local directory here\n",
    "image_documents = SimpleDirectoryReader(\n",
    "    input_files=[\"./llama2_violations_charts.png\"]\n",
    ").load_data()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### General Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image you've provided is a bar chart displaying data grouped into three categories of online violations: hateful and harmful, illicit and criminal activity, and unqualified advice. Each of these categories has a number in parentheses, indicating the sample size of reported incidents for each type of violation (395, 728, and 311, respectively).\n",
      "\n",
      "The x-axis of the chart specifies various social media platforms or services, such as \"Video sharing\", \"Social networking\", \"Gaming\", \"Dating\", \"Forums & boards\", \"Commercial Websites\", \"Media sharing\", \"P2P/File sharing\", \"Wiki\", and \"Other\". It appears to measure how often these categories of violations occur on each type of platform.\n",
      "\n",
      "The y-axis measures the percentage of violations reported, ranging from 0% to 60%.\n",
      "\n",
      "Each platform/service has three bars corresponding to the three violation categories, showing their respective percentages. The bars also have error bars, which typically represent the variability of the data, such as standard deviation, standard error, or confidence interval, indicating the precision of the estimates.\n",
      "\n",
      "The chart has a legend indicating the color corresponding to each of the three categories of violations. This visual representation helps to compare the prevalence of different types of violations across the different types of online services.\n",
      "\n",
      "Without specific numbers, it's not possible to provide exact percentages, but we can observe trends, such as:\n",
      "\n",
      "- \"Forums & boards\" and \"Social networking\" platforms have notably higher percentages across all three types of violations compared to other platforms.\n",
      "- \"Commercial Websites\" seem to have lower percentages of reported hateful and harmful activities and unqualified advice but higher percentages of illicit and criminal activities when compared to other platforms like \"Wiki\" or \"P2P/File sharing\".\n",
      "- \"Gaming\" appears to have a moderate percentage of hateful and harmful violations, lower levels of illicit and criminal activity, and relatively higher levels of unqualified advice.\n",
      "\n",
      "Overall, this chart is a visual tool that likely aims to inform about the prevalence of certain types of online violations across different digital platforms.\n"
     ]
    }
   ],
   "source": [
    "query = \"Analyse the image\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "As you can see though the categories hateful and harmful, illicit and criminal activity, and unqualified advice but it hallicunated with x-axis values with -  \"Video sharing\", \"Social networking\", \"Gaming\", \"Dating\", \"Forums & boards\", \"Commercial Websites\", \"Media sharing\", \"P2P/File sharing\", \"Wiki\", and \"Other\"."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specific Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The provided image is a bar graph with three categories along the x-axis: Hateful and harmful; Illicit and criminal activity; Unqualified advice. It shows a comparison of two types of models – Llama2 and Vicuna – across these categories in terms of violation percentage, which is represented on the y-axis. For each category, there are multiple bars representing different subcategories or criteria.\n",
      "\n",
      "The error bars on each bar indicate the confidence interval or variation in the percentage of violations.\n",
      "\n",
      "From the graph, it looks like the Vicuna model generally has a lower violation percentage across all subcategories compared to the Llama2 model. This suggests that Vicuna may perform better in terms of producing fewer content violations in these areas.\n",
      "\n",
      "However, without knowing the exact context or details of the models and the evaluation methodology, my interpretation is based solely on the visual data presented. If you have specific questions about each category or subcategory, or the implications of these results, feel free to ask!\n"
     ]
    }
   ],
   "source": [
    "query = \"Compare Llama2 models vs Vicuna models across categories.\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "It answered wrong by saying Vicuna model generally has a lower violation percentage across all subcategories compared to the Llama2 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From the provided bar chart, we can analyze the violation percentage for both the Llama2 and Vicuna models in the \"Hateful and harmful\" category. To determine which model does better (i.e., has a lower violation percentage), you would look at the height of the bars corresponding to each model within that category.\n",
      "\n",
      "In the \"Hateful and harmful\" category (which is the first group of bars on the left), you can compare the blue bar (representing Llama2) to the light blue bar (representing Vicuna). The model with the shorter bar in this category will have a lower violation percentage, and hence, better performance with respect to minimizing hateful and harmful content.\n",
      "\n",
      "Please note that I cannot physically see the chart, so I'm unable to specify which model has the lower violation percentage. If you provide the actual percentages or describe the relative bar lengths for Llama2 and Vicuna in the \"Hateful and harmful\" category, I could then tell you which model performs better in that respect.\n"
     ]
    }
   ],
   "source": [
    "query = \"which model among llama2 and vicuna models does better in terms of violation percentages in Hateful and harmful category.\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "It failed to accurately capture the information, mistakenly identifying the light blue bar as representing Vicuna when, in fact, it is the light blue bar that represents Llama2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's inspect by giving more detailed information and ask the same question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the image you provided, which shows a bar graph for the violation rate performance of various AI models in the Hateful and harmful category, we can analyze the light blue bars that represent the Llama2 model and the dark blue bars that represent the Vicuna model.\n",
      "\n",
      "Based on the visual information given, I can compare the two models within this category by observing the heights of the light blue bars (Llama2) versus the heights of the dark blue bars (Vicuna) for each subsection within the category. A lower bar indicates a lower violation rate, which could be interpreted as better performance in minimizing violations for the given criteria.\n",
      "\n",
      "Since I can't give you the exact numbers or percentages, I would describe their relative performances. It appears that for some subsections, the Llama2 bars are shorter than the Vicuna bars, suggesting that the Llama2 model could have a lower violation rate in those areas. Conversely, in other subsections, the Vicuna bars might be shorter than the Llama2 bars, indicating a lower violation rate for the Vicuna model in those areas. The exact subsections where one model outperforms the other would depend on their relative bar heights, which should be compared individually for the given information.\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"In the image provided to you depicts about the violation rate performance of various AI models across Hateful and harmful, Illicit and criminal activity, Unqualified advice categories.\n",
    "           Hateful and harmful category is in first column. Bars with light blue are with Llama2 model and dark blue are with Vicuna models.\n",
    "           With this information, Can you compare about Llama2 and Vicuna models in Hateful and harmful category.\"\"\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "It did answer the question correctly."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chain of thought prompting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examine the Image: The image is a bar chart depicting violation percentages in three categories: \"Hateful and harmful,\" \"Illicit and criminal activity,\" and \"Unqualified advice.\" Each category has two bars next to each other representing two models named \"llama2\" and \"vicuna.\" \n",
      "\n",
      "Identify Relevant Data: We are specifically interested in the \"Hateful and harmful\" category, which is the first set of bars on the left. The two bars indicate the violation percentages for \"llama2\" and \"vicuna.\"\n",
      "\n",
      "Evaluate: By visually inspecting the bar corresponding to \"Hateful and harmful,\" we see that the blue bar (indicating \"llama2\") has a lower percentage than the red bar (indicating \"vicuna\"). The exact percentages are not clear, but we can discern the relative performance between the two.\n",
      "\n",
      "Draw a Conclusion: From the data visible, \"llama2\" has a lower violation percentage than \"vicuna\" in the \"Hateful and harmful\" category, thereby doing better in that respect according to the given image.\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"Based on the image provided. Follow the steps and answer the query - which model among llama2 and vicuna does better in terms of violation percentages in 'Hateful and harmful'.\n",
    "\n",
    "Examine the Image: Look at the mentioned category in the query in the Image.\n",
    "\n",
    "Identify Relevant Data: Note the violation percentages.\n",
    "\n",
    "Evaluate: Compare if there is any comparison required as per the query.\n",
    "\n",
    "Draw a Conclusion: Now draw the conclusion based on the whole data.\"\"\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "With chain of thought prompting it did hallicunate with bar colours but answered correctly saying Llama2 has lower violation compared to vicuna in Hateful and harmful though for a section Llama2 has higher violation compared to vicuna."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image2 - Llama2 vs Mistral model performances across various NLP tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x785d06ebb820>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = Image.open(\"llama2_mistral.png\")\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_documents = SimpleDirectoryReader(\n",
    "    input_files=[\"./llama2_mistral.png\"]\n",
    ").load_data()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### General Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image appears to be a set of four line graphs that compare the performance of different natural language processing (NLP) models across four different metrics: Natural Language Understanding (NLU), Reasoning, Knowledge, and Commonsense. The models compared are LLaMA 2, LLaMA 13B, Mistral, and GPT-3.\n",
      "\n",
      "Each graph plots the performance metric (on the y-axis) against the model size measured in billions of effective parameters (on the x-axis). In general, these plots demonstrate that as the model size increases, the performance on each metric improves.\n",
      "\n",
      "Here are specific observations for each graph:\n",
      "\n",
      "1. Natural Language Understanding (Top left): All models show an increase in NLU performance as the model size increases. Mistral is depicted as the highest-performing model at each size benchmark.\n",
      "\n",
      "2. Reasoning (Top right): Again, larger models perform better on reasoning tasks, with Mistral consistently outperforming the other models mentioned.\n",
      "\n",
      "3. Knowledge (Bottom left): Similar to the NLU and reasoning tasks, the trend is for better performance with larger models, with Mistral leading across the different sizes.\n",
      "\n",
      "4. Commonsense (Bottom right): This graph follows the same trend with the Mistral model performing better at each size level compared to the other models.\n",
      "\n",
      "Based on these graphs, Mistral appears to be the top-performing model in all four metrics, suggesting that it might be more efficient or effective than LLaMA 2, LLaMA 13B, and GPT-3, at least within the scope of the parameters and tasks represented here. \n",
      "\n",
      "On a technical note, it's important to mention that the y-axis shows different scales for the metrics (ranging from 45-75% for NLU, Knowledge, and Commonsense, and from 50-72% for Reasoning), which suggests that these tasks might have different levels of difficulty or different benchmarks for success.\n"
     ]
    }
   ],
   "source": [
    "query = \"Analyse the image\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "It did answer the query but hallicunated with NLU task which is MMLU task and assumed Mistral is available across all different model parameters."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specific Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image you've provided contains four graphs, each plotting different performance metrics for evaluating language models. It compares two models: Mistral and LLaMA2 across four different aspects—MMLU (Multi-tasking multiple-choice), Reasoning, Knowledge, and Commonsense. In each graph, the performance metric is plotted on the y-axis while the effective model size in billion parameters is plotted on the x-axis. Here's a summary based on the trends observed in the graphs:\n",
      "\n",
      "1. **MMLU (Top Left Graph)**: LLaMA2 shows a steeper performance curve as the model size increases, starting at around 61% for the smallest size and reaching upwards of 66% for the largest model. Mistral also shows performance gains with model size but plateaus earlier, reaching a level just shy of 62%.\n",
      "\n",
      "2. **Reasoning (Top Right Graph)**: In the reasoning task, LLaMA2 again has a steeper improvement curve, starting from about 61% and surpassing 72% for the largest model. Mistral, while improving, seems to plateau near 70% for the largest model.\n",
      "\n",
      "3. **Knowledge (Bottom Left Graph)**: This graph reflects a similar trend to the previous ones, with LLaMA2 beginning at a lower performance around 46% and eclipsing 52%. Mistral starts higher at around 48% and appears to plateau near 52%.\n",
      "\n",
      "4. **Commonsense (Bottom Right Graph)**: Here, LLaMA2 starts its performance at approximately 62% and reaches just above 66%. Mistral seems to start at a slightly higher point than LLaMA2 but ends at a similar level to LLaMA2's largest model.\n",
      "\n",
      "Overall, the LLaMA2 model appears to show a greater degree of improvement in performance as the model size increases compared to Mistral across these metrics. Meanwhile, Mistral starts at a higher performance for some metrics but tends to plateau earlier, suggesting that LLaMA2 may scale better with size in terms of performance gains.\n"
     ]
    }
   ],
   "source": [
    "query = \"How well does mistral model compared to llama2 model?\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "Incorrect answer and percentages are not accurate enough and again assumed mistral is available across all parameter models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This image appears to be a set of four graphs, each comparing the performance of three different language models on various tasks: Mistral, LLaMA-2, and a hypothetical \"Size 7B\" model. The graphs are labeled with task-specific performance metrics: \"MLM U\", \"Reasoning\", \"Knowledge\", and \"Commonsense\".\n",
      "\n",
      "The x-axes on the graphs represent model size in terms of the number of parameters, with three points that likely correspond to the sizes of the models being compared. The y-axes represent performance as a percentage, which could mean accuracy, precision, recall, or another relevant performance metric depending on the specific task.\n",
      "\n",
      "The graphs appear to show that Mistral performs better than LLaMA-2 and the Size 7B model across all metrics. This indicates that within the context of these measurements and tasks, Mistral is a stronger model. The exact nature of the tasks or what \"MLM U\", \"Reasoning\", \"Knowledge\", and \"Commonsense\" specifically refer to are not detailed in the image, but they likely correspond to standard NLP tasks designed to test understanding of language, ability to reason, knowledge recall, and commonsense reasoning, respectively. The performance improvements are depicted as increasingly significant with larger model sizes.\n"
     ]
    }
   ],
   "source": [
    "query = \"Assuming mistral is available in 7B series. How well does mistral model compared to llama2 model?\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "Now with giving the detail that mistral is available in 7B series, it is able to answer correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chain of thought prompting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examine the Image: The image contains four graphs, each graph compares the performance of three different models—Llama 2, Mistral, and an unnamed third model—across different metrics: mAP@L (mean Average Precision at L), Reason@L (presumably a reasoning score at L), Knowledge@L, and Comprehension@L. Each graph shows performance as a function of model size (in terms of billion parameters).\n",
      "\n",
      "Identify Relevant Data: We need to focus on the Mistral and Llama 2 models across all four graphs to extract the relevant data.\n",
      "\n",
      "For mAP@L:\n",
      "- Llama 2 reaches above 65% when reaching 70 billion parameters.\n",
      "- Mistral starts at around 5 billion parameters with about 48% and reaches almost 65% by the time it gets to 34 billion parameters.\n",
      "\n",
      "For Reason@L:\n",
      "- Llama 2 reaches about 68% when reaching 70 billion parameters.\n",
      "- Mistral starts at around 5 billion parameters with about 52% and reaches almost 68% by the time it gets to 34 billion parameters.\n",
      "\n",
      "For Knowledge@L:\n",
      "- Llama 2 starts at about 45% with 1.3 billion parameters and reaches just slightly under 60% by 70 billion parameters.\n",
      "- Mistral is not plotted in this graph, so no direct comparison can be made.\n",
      "\n",
      "For Comprehension@L:\n",
      "- Llama 2 starts just above 40% with 1.3 billion parameters and reaches just under 60% by 70 billion parameters.\n",
      "- Mistral starts around 13 billion parameters with about 52% and continues above 62% by 34 billion parameters.\n",
      "\n",
      "Evaluate: From the graphs, we can see that Mistral and Llama 2 are very comparable in performance in the metrics where both are shown. However, Mistral is not displayed on the Knowledge@L graph, so no comparison can be made for that particular metric.\n",
      "\n",
      "Draw a Conclusion: In the graphs where both models can be compared (mAP@L, Reason@L, and Comprehension@L), Mistral appears to perform comparably to Llama 2, reaching similar levels of performance at smaller parameter sizes relative to the plotted sizes for Llama 2. This suggests that Mistral could be more parameter-efficient. However, since there is no data for Mistral in the Knowledge@L graph, we cannot draw a complete comparison\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"Based on the image provided. Follow the steps and answer the query - Assuming mistral is available in 7B series. How well does mistral model compared to llama2 model?.\n",
    "\n",
    "Examine the Image: Look at the mentioned category in the query in the Image.\n",
    "\n",
    "Identify Relevant Data: Note the respective percentages.\n",
    "\n",
    "Evaluate: Compare if there is any comparison required as per the query.\n",
    "\n",
    "Draw a Conclusion: Now draw the conclusion based on the whole data.\"\"\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "There is hallicunation with number of model parameters and percentage points though the final conclusion is partially correct."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image3 - Performances of different LLMs across various NLP tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x785d05b2b3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = Image.open(\"llm_analysis.png\")\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_documents = SimpleDirectoryReader(\n",
    "    input_files=[\"./llama2_model_analysis.png\"]\n",
    ").load_data()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### General Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image appears to be a table containing numerical data, which seems to be a comparative analysis of various models across multiple parameters or tests. Each row represents a different model or configuration as indicated by names such as \"MPT\" and \"Falcon\" followed by a size specification like \"70B,\" \"7B,\" or some other parameter. The columns are labeled with test names or evaluation metrics, such as \"Size,\" \"Avg,\" \"AQuA-RAT,\" \"LogiQA,\" \"L-SAT-AR,\" \"L-SAT-IC,\" \"L-SAT-RC,\" \"SAT-en (w/o Psg.),\" and \"SAT-math.\"\n",
      "\n",
      "The data is likely related to performance scores of these models on these tests, where higher numbers probably indicate better performance. Without additional context, it is difficult to provide a comprehensive analysis of this data, but it seems clear that it is intended to provide a performance comparison between different models on various tasks, possibly in the field of machine learning or artificial intelligence evaluations, where such models are typically assessed on reasoning, comprehension, or problem-solving capabilities. The \"70B\" and \"7B\" demarcations could refer to the size of the model in terms of the number of parameters, commonly used in assessing language models.\n",
      "\n",
      "A deeper evaluation would require further insights into the specific nature of these tests and models, along with the intended use-case for which they were being compared.\n"
     ]
    }
   ],
   "source": [
    "query = \"Analyse the image\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "It did not analyse the image specifically but understood the overall data present in the image to some extent."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specific Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the table you've provided, the models are compared based on their performance on several benchmarks, including SAT-en (SAT Analogies, or \"SAT\" in the table). To determine which model has higher performance specifically on the SAT-en benchmark, you'll need to look at the corresponding column.\n",
      "\n",
      "In the SAT-en column (second from the right), the two models with the highest scores are:\n",
      "\n",
      "- LLaMA1 65B: with a score of 57.9\n",
      "- LLaMA2 70B: with a score of 63.4\n",
      "\n",
      "Between these two, the LLaMA2 model with 70 billion parameters shows the higher performance on the SAT-en benchmark with a score of 63.4.\n"
     ]
    }
   ],
   "source": [
    "query = \"which model has higher performance in SAT-en?\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "It did answer correctly but the numbers are being hallicunated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The table you've provided shows performance benchmark scores for different model sizes across various AI models such as MPT, Falcon, and LLaMA on different tasks, such as Aqua-RAT, LogiQA, LastAR, SAT-en, and SAT-math.\n",
      "\n",
      "For the SAT-en task specifically, you asked which model in the 7B series has the highest performance. To find out, we need to look at the column labeled \"SAT-en (w/o Ps_8).\" In the 7B series of models, here are the scores:\n",
      "\n",
      "- MPT 7B: 37.1\n",
      "- Falcon 7B: 37.3\n",
      "- LLaMA 7B: 63.9\n",
      "- Model2 7B: 37.4\n",
      "\n",
      "The LLaMA 7B model outperforms the other 7B models on the SAT-en (w/o Ps_8) task with a score of 63.9.\n"
     ]
    }
   ],
   "source": [
    "query = \"which model has higher performance in SAT-en in 7B series models?\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "It did pick up the model names and answered correctly but recognised Llama series of models and values incorrectly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chain of thought prompting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To answer which model has higher performance in SAT-en in the 7B series models, I will follow the provided steps:\n",
      "\n",
      "Examine the Image: The categories in the image include model names and sizes, and performance scores for various tasks, including the SAT-en category.\n",
      "\n",
      "Identify Relevant Data: The relevant data for the SAT-en category are the percentages listed under it for each 7B series model.\n",
      "\n",
      "Evaluate: I will compare the SAT-en percentages of each 7B series model.\n",
      "\n",
      "Draw a Conclusion: The SAT-en scores for the 7B series models are as follows:\n",
      "\n",
      "- MPT 7B: 63.1%\n",
      "- Falcon 7B: 73.4%\n",
      "- LLama 1 7B: No data present for this category.\n",
      "- LLama 2 7B: 76.6%\n",
      "\n",
      "Based on the data, the LLama 2 7B model has the highest SAT-en performance among the 7B series models with a score of 76.6%.\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"Based on the image provided. Follow the steps and answer the query - which model has higher performance in SAT-en in 7B series models?\n",
    "\n",
    "Examine the Image: Look at the mentioned category in the query in the Image.\n",
    "\n",
    "Identify Relevant Data: Note the respective percentages.\n",
    "\n",
    "Evaluate: Compare if there is any comparison required as per the query.\n",
    "\n",
    "Draw a Conclusion: Now draw the conclusion based on the whole data.\"\"\"\n",
    "\n",
    "response_gpt4v = openai_mm_llm.complete(\n",
    "    prompt=query,\n",
    "    image_documents=image_documents,\n",
    ")\n",
    "\n",
    "print(response_gpt4v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "With chain of the thought prompting we are able to get right conclusion though it should be noted that it picked up wrong values."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Observations:\n",
    "Observations made based on experiments on Hallucination and correctness. \n",
    "\n",
    "(Please note that these observations are specific to the images used and cannot be generalized, as they vary depending on the images.)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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EBAFBQBAQBAQBPyEQA5lEUOnSpf0UpAQjCAgCgoAgIAgIAoKAICAICAKCgCDACOzbt49fKYqVcERFRRmGSh+H1VJeAoWAYB0oZMM3XKkz4Vu2kjNBwB0C0u7dISPmgoAgIAiEFgJRdEPvoUq2iCCGVulJagUBQUAQEAQEAUFAEBAEBAFBIIQREAYshAtPki4ICAKCgCAgCAgCgoAgIAgIAqGFgDBgoVVeklpBQBAQBAQBQUAQEAQEAUFAEAhhBIQBC+LCq1KF6KabgjiBAUjarbcS3X03UbZsAQhcggxpBPLmJapdmyhnzuDLBupttWpE2bP7P23SJvyPqYQYWggkJBC1aaPbfyDaGNDIk4eoYEH/4RJtzK7uu4+ocWPfxrMsWYhuu803t/5LpYSUWQigfpQpE7jYMV6eOUM0YYLrOMqVI2rXjgjzTKTFE6W2LjuGBf87dxItWuRoE+HfUMIBMmAwfnjKL/AYuMf6hRcslt27LZbkZF0u+H/tmsXyyy8WS65coVU2L76o89C3r+d0V6lisfz5p8WSmGjLM942bbJYChTw7DdQZXXHHRbLV1/Z4j5zxmK5cMH2Hah43Yfrvs6495OZ6fVf3P36WSxXrtjXjUuXLJZOnfwXh68Y9u+v0zF0qI47IcFiOX/elrb58+3tfQ3X0V0otIkVK3ReCxfO+HJwxCt8vyO33aNMhw93HheuX7dYli+3WHLm1PVu9GhdD9u0SV89vHrVYkHY6R1ny5SxWP79V4dl6xkslhMnLJa6dV2n8bPPbO4x7pnrc+PG5lCc38+ds1jKltXmc+fa+zWHk9HvqUlTliye071mjXO+T560WEaN8uwvo/PsKT7HPG7ZovPUtWtg8rBunQ4fYxanK1s2i2XhQj2nNCOKuj9hgs0du/e1Lu/ZYw7N+f3ZZ/UcDzaZMW5zfjL/aXRqJoqJcP4zqLI/d65e5UMz+OUXog0biLDy16SJXvk7cIAoX76gSrLHxCjFmoYLfrpyXKoU0W+/6Z2Df/4hWrWKqFgxorp1ie68k2jrVr1KlJjoynfgzJCOGFPrOHzY/jtwMUvIZgQ6dCB65x2i69eJNm8m2rSJqGZNIuwKffqpLpMZM8w+Avt+4gTRxYtEeIKef54od269uvf550RHjxI1bGiz165S9z9U2gT6I2CRkpK6/IlrQcAXBP78k6hqVaKrV4l++olo+XK9S9W1K1GDBrrNFS/uS0i+uTl5UoefnOybe1euWrQgmj9f7yjs2UP0/fdE58/rXbDbbyf68Ueil18mGjXK5htjXceOui3NmkW0d6/NDm/btxMtXGgze/RR/c5mu3bZ7ELxbc4corZt9TjvmHfOD88hOM+YB1WvTjRyJFF8PNGzz7LL4Hy6yuN//xFVrEh06JD/0wxJkRo1iP76i2jyZB1+4cK6zWC8OnVKzzHRrlAvH3+caPBgopIl9a4YfKSmLn/4IdE99+h4mjXTc75164iOH9dma9YQffYZEeZRb79NhHouZCDAzBheM587dObAwzNNzlg3bKhLAisRWP025zs62rZ6MGCAvZ3ZXbC9Y/UShB0MV2lDvrCzBBo82NkNVgJBWCly5T+QZlhVzNwdL8c8O9eZQOY/WML+8UddB3r2tMcDO1CgP/6wN8/odH/xhU4HVqn9Ebe0Cf/g6I+yCI4wIrPdYzUedOSIxZI3r3OdmDXLYuneXZv7awcsveWNHTneqTfvOnC42P2CNAt22W67zZant95SWbU89ZTNjP24esL1hg32blOz2+QqzECY+ZqmBQt0/m+6yT5P5jStXavdmM0KFrRYMF8KrnHadR58yaM5b+l9//VXjVft2rb08I7bkiU2M44Hu76QsqpUSdultS4jPOzCglD+HD4/GYfHHnO2Yzfh/bSyXAoj65cwYBlZIZwH1S+/VOVhgQiiqwqIhgERhmHDbPYQ/YHoXlKS7tRPnbJYOnSw2WOr9+JFi2XgQIvl2DEdPjqsefMsFkz0OB4MDIcP6zAwQEAE0jxAQDwCA+G332o3SAfibtHCYjl40CYuCbFJuImN1WF7Y8Duu0+n6e+/bWnhNOGJ7XKkH5Q9u8WCDhffH31k7x6T8L17bWbY6kceeTCEuNo779jsETYYPnTcIKQb/iF2CDuIjzAhvkaNLBakEeacPkwKIAYDPEGI4/33bfa+Yo/OisPAE6IWefLYwuH4IrV9MhNurtfABPX3++8tlv/9T2OFAQRlNXOmrpOY5KB858yxx9Jbm0HYRYvqCQ7KA+0B9atZMx0O0oF4MPnDIAd7EOoa3JntuezQDtB+kCa00U8/tbURdsPP1LYJ+Nu502L56y/7fAIXpLNUKW0eiDbx8cc6DhYTzvg2YZ9nxjC8ns5jRXjlz7kM0bbRVtCmWMzQU56ZAUM7w9jFfh0nms89Z7FgYQ3E49zdd9viR3/Ci31o3xgz27WzhYlxAmK3PL45pgntAfTGG7YwHd2wOOHmzdrN9u3aD/4jfMT5yCPu/SM8kDsG7OuvLRYwK5gTII///KPHanM6kAaeNyBOpIX7N7hDGOhTzH7wfuiQxTJ9us28Rg2NF+JBfL//bptrYP7ADJinNDmOtci/K5FmVwwY0oS0gzBXQN+PdGOOxGM/L9xhXrVnj64bGKtXrbJnEFDuSOfs2bockCf0qejT4BfijqhX8MthIn74w1GFadP0OA43wAnHLxg/d3lEvUV+ixfXbjkszCPQd4NOn7YvG4QJe4wjiAtzMSxwI62YQ8Ie7QflCjtOA9c7lDubeXqmpS5zeJ4YsPLldb4gHsnuI+tpFJCJrF+ROsHLnMJ3HlTBAIB4MuMtXRgEuJGioS1dqjtBhNGqla7cYLyYcL4KHSQ6ShAGK8SBAQgNGYTB4Oef9TsaMKcFKyNM6NjQCWFiB0JHhcGAO32Y8QDkjQGDO5A7phPp45UcMHslSmj3GATN+KBzRP7Y7LfftDuYLVumOyuYYOILN9hhBGGCjbDQ8YGQL9jPmKHzhbz98IN2j4EbeHMc6OhBYFwhU83ngBYt0m58wX7kSBWE6kzhD2GBXHdOznWG0xLOTzDKINTR1astapUYzLhjnjFQMqEcUdYYCEEffqjd+9JmMJBjUARhdxb1GnGjriBe7ECDMMhjlR51DwTma+pUe3ukEYMjCGGgXqKtgt591zkPcJ/aNgE/yC/SasZk8WIdDwY8mAeiTaBtgMCwIo6MbxP2eTbnP3zeI6/dY2IPwrjjSzkyAwY/aKdYtOHzxNznc7vFuAZ7TGDZPS94ob/A+IY4OUy0W5hhgovxADRkiOt0gdkBcXtwl3akkceaffu0H/xH+JhwN2/uOnwOD27dMWCwQ5qRXu6bEAf7xY4I4oEbzDl27dLv+K5bV7sDwwCc2A8/ETYWCPGN3SrGeP9+3fYRBn6gWrVsDBi+Ye4qTXv2wFYTcEb+fWXAcP4WWPIOGDBhwjwH+QcjzWfRkScspB49ql1hzM6XT+cHcTPt2GGbWyE9IPTbPEdD2IwJ/HGeEZ+5nrBUj7s8YnEQVK6cLQ0cFsqFxwqkk+PD3ASEfGPewvMOmGH+BneY04HMZ9ixAA3yNNfiOPBMS11m/54YMLjBPAplwO4j62kAYCLrlzBgGVkhnAdVdA5oVObKiI6oc2f969rVYsGPRZ3efluXIiZB7IdXF7DaDjNmAjCxZze8EgJmDGbbtulwzKKNL72kzbBdDDfMgH3zjf7GCsukSdrNq6/awgaTBMJkD/68MWB8SBTil5w+xyfnE6s+vjBg6PhB5gkpT7zRuWGiPnKkcmJ55RVbvFgtRafAOwaOIohmBgyHvUHmHTFMzlF+IBxc9QV7nhRzBwxcMZhhMu+IQyS3TzA2ZkI5AqeOHW04mRkwFmUBniziCsaK65KnNoP6DMIOKpcB6gkIu0o8keOdaKyYgqpX1+4d7TFJwYSHVzmxE4XJF9o70sRx8DO1bQL+vDFggWoTZgYsc9qEM36MY/g8nceK8Mmb6/JD2wLxIqG3/DKzhHbAi4Zob2h3YLTgH0wWFuO4r4UZFr1APCl1xYBhgstKOXh85d0rx3RhYsmMlaOd+Zsnt9z+uX+rW9c1Hma/eAd5YsB4PEX/h7kAiHcSwVCg/8RYzeE+/LA244m+LwwY91PmnR5IijAD4ciAeUoTi6Vxv83pMj95BwyLafhhLgKsQbwrxwwYGCj2C6kZENxi/sDmPBZgwRlmzIBhl5TdMINpltDBLhmIy4r98cIr/GL3D0wasEAZwMxVHl0xYAgb8xNOA+ZpICwaY14BQhnyogHc8QIyM2CYM4Jee80WDisx4fkNh+/umda6jPC8MWBgZM1MrLs0hKe5AZCJvCifNKZ9QhmCANR0OqoCbdqUaOZM/YOiAfygqANUr5560P79RM89p3/NmxMlJRHhsKWZ+OAqzHAoGJQrl35CFSkIqn05HD7wCnXwZnrvPf0FhQgDBhDlyKEPweIwLA7B9uql7WHuC6F7AHF8+sv+P/IDgopeXwgYgA4etOUHacMBa8QDtcA//KDdvPiiVgDSvz9Ry5ZERYqQwlPbuv+Pw6mgV17RT/yHkpA//tDfUDvM5Al7KJQAbdtGtGQJEQ5XQwEDlI8I2RDo04eoRAmisWOJoKgF9QaHhXGo9403bO7wBrW7fJAb9fTLL7U92osvbaZWLe0edYIJB70rVyZ6+mk28e0JNcC4TgFKZvig9bVr+tAzlOtAuYAjSZvQiEibcKwZkfPN7SK1101gfIRyARDaG8YAKGgAQREG2i/GuN9/J/r1V91XwA5quN3R9OlaOQbs//1X+4ECIFeE/saXcSprVu07EMprduzQykoQA9KzYoWOC4qL0B9BeQUUInz9tTbH/8WLtXIQKGcoWtRm7ukN6vLPniUaM8bmatkyIihOcSRPaXJ06+27Wzci/DBWAkcoYurRw95Xly62byj3AI0ereuD/tLzFdQJ81iLuYZ5PIHSJ9CIEfqJ/198od9btbKZoc/GXI3p77+1kgnMNx58kE19e0IBDJS0MH3yiX6DkhaO86WXdH1mN5xH/i5dWr9ByQcTz6N8bVOBrMvHjhHFxnLKIvsZHdnZD57cY8CA1r2yZW1pgoaaiRNtP56cwQWYBRA0Qo0fb/uhU0LDNzNhYNKYMAFEOMzs8WDw+uu2MEaN0q4LFGBf+skMBr4w0KDjRoeBCSbS+dBD9u69fSF/IEdGT5vq/zw4Ig5fiAfHSpVs+QE+3ClhIr16NdGbb2ocwDxOmkR0+TIRNBX5Qpj8g9DRmunbb/VXhQo2U0/Y9+unB0OUAZi2r77SDDTMhTQCuJsHzArax7Bh+p4cdN5gxlCPHbVfMVPL+O3dq9+gocmXNsMLEkeOcAh6IgNtnKklMPsgaAs0E9J07pzZxPburzZhXtSQNmHDV96CHwFeJIQ20dQQNAebCRo6uR3Ur68ZqKee0uPNXXcRdeqkXXuaDDqOO2A6OExzXHhHu8aCi3kMd3SDbyyyXbhAhLHY3+Q4JjFDijw+/LCODeOfI/38szbhBUxHe/M35g5g1qDl0ZGYaTGbe0qT2Z0v7/ffT4QfGCfkycxssX9zmfGchMdmdoMnGEMsRPO9cqdPm23tNfixDefZXGdYIy67wRNalEGprcNcXtq31lqId6SRxxNoAzUTtGSCYWLCwgPoppvUQ/1bv16/g5HzhQJZl3HXnjm9vqQnXN0YTUkoGBCAynkQVvuZsIIxaJDtZ14xQwcOat1ar86DsTD/zJ2Cp44eDQHhmv3y+x136Dj4v1kVPHYfHnhAq9sGM4NLaJnJcTdAcTj8/OYb/ea4ggUGBB0cBrM6dbSblSvZl/NFlXFxNjvON9LH+TA/p07VbocM0Z1a9+6aIcNkHitJPXvawnL3xpNzntCzOwxKIDOj6gl72EFdLBgMrLJhoEK+oXYdq5WRTlitBaOycaM9EqizYMaw2opFCzDR7ghXGoAwMfClzWBVFMSDsv7Sq668GMBm3p5QuQvKn18/+T8WHHCprKvVyLS2CfOVCYinUCGOzaYSX9qEDRN5C14EtmzRC3tob+aFRG8pNo+Pjm5ffVX3FUOH6j4WUhro+71RaiaKPOk272A4ho840VbTsqDjGJarb08YMHNkXiDkMHiRBmMXxkIssPIiLdxAAoEJmGCX0tVu2c03syvb01OabK58ewPziJ+rnTZXIYDJAuE6A0fC2IC08bwG+XZFruqAeY7jaqyGenkQmKPUkLs0IAxIf4DMu3b4BkNjLitmeDkNcMM7np0748s7BbIuY76DBW8ho9wEhOBAADtIaHy4i8EVE4DVOvMkizuWxx7TnTk6dKxaQLSCxQ58yRlWfRAuOk6EgR92vjDpZZFDV+FgFQoEUQQwM3CPuyRA5nRqE9f/167VIiO4DR5iXiAMuGBAMFnGQIxJKu6T2L2biFeozIwP/JonywgThPRxfvDEVj5WgW65Re94YecOK4IffaTdYkcFBIYIhE7X3KlpU/0fIiygvn31k/+zKMDSpWzi+QnM0PkjLZgg4D4OZt5YFM5zCOFtC3FCrAhixRj3gZkJ9QJ1Fm2GMYM97j4xE4uGgIH3pc3wgNm7ty0UxI17TswipzZb92+ov0ifY5oWLdK7reaFAw4ltW0C/iBegjbAq7KYPKGeM0mbYCTkGSoIQGQM9RmTTkdJDOQBovlYbHM1+XWVR+xsoy1OmGBbfefdEVfu02KGcRC7bhirhw93DgHjzbRpOh2+7kQ4h5J2E0zMMdZgV8Z8nyikDJA2jHnYPUK/BQaDRd4QI0Q3zYTxGGM1L5DCDneyOfZ1Zj/u3pnB8eedbhwXH9nAfY1mQjrBgGHOlF7CQrF5fMK8AZJJIEi1gPyRRw4LiwhmMotNwhzjIeIzzyEwL0TZ3nQTEY8H5jCQh507bTt3garLKGMsVCMuIQMBPg+GV901oJuSX2AxcI01DrLzIVYozoAWG1YOweXEqnVxIBNu8cNheGhmggpeEBRkIP2sCIIPGXOe4IcPbPbtq/1AgQTUuOJAMw5ggvigKSvhgKIDDgNKCkCIGwd6ESennbUueVPCgbCgMYrjw+FZHEhmxSA6Bovlzjtt8fKhV9y/BIUJrAUIB2Y5bay5COHhUC0OX4P4MC0OyYLgF2EgneyHlZywpiTEA/dmJRxQpMBphhIGKEdh7ZF8ANgX7PmwObTHQWMSMIRyBhweRxycH/10XWfs3Tj6Cf1vxgjlhXqBOgqtk4w/7gkDBmYlHFBi0q2brpvwt3KlduNLm8EhZdRjlAMOQ0MBB7RswQwHuh2VbHhTwoH2CoLmNdzzA1XXIBzmdld2qW0TfPgcygGAl1ntMWtB5PrtzzZhVsKROW3CPYbusA0988hs9ygntGMQ+nZoQIXCB2ge5b4ZYwGUarASDoyf5vJFf4F2CzP0ASCMZVAjzte+wAxKA+DGlRIOxzAxxnpSIAANhui/QVA2APXkSDPGHh4foeTKnE5/KuHgvHD4rGwCCjJgxlpWobEP/RswZW17SAfcPPCASr5SYAJ7KL3gtLMWRKjKhxmwwDU1yCdf6wLfZiUc3tIEZUogzBtwvxu0G3L6+clKOPjb1ZP7QUc7qF4HoX/E/WyTJ9uUZLAyEtQls7IyhIErdUBmrYxQtgTi6094PoIyh6IwjPvc/zJWCMtVHl0p4XBMA98Pi3JEOKhHIChyQb3iezJhxnM6uOO2g7kLvvHD+MXzJSingSITzBtxrQ8rHIH2RVYckpa6jHg8KeFAXCDMOzldkfU0ADKR9ctXBuz1102+Ta9oiJhsmDXN+Aos7l8yq8z01Z/ZnSkpbl9ZLajZX+a8ux9Uce8IGhLwZEJHB61vrEmI04zBgTUBwS2YKLOK9mef1SGYNfvAL8JDA+Zw0CnzoAEfYDbGj7fZY/ADmRkwTFSRJiaEiQaNjh3vmJCh8wZ5a2zoJNB5cicAP5hgo8NEWPhxBwQNP5gcM0HLExgYdIScn1tvtU8b8ob8mrUsoRM2xwc3Y8fawoAGISZclAltekgTx4E6yxqm4A5pxCSbNVv5ij06aTP2yIfre2Dc1xlOU7g+caeaeXAH3mgfYH44z8yAYYBhPFEm0CLFAwrcemszcIM2yAMr4kJ4PGnCAA7CRdBwyxcxV6umvx3toX2M24/2qeumq0kG5wXP1LQJuGUGC3GgXvOiA2t9C0Sb4EktT1Ayvk3Yyt+MXXi9R267RzlicczcT6N+o11j4smaRUeOhKnF4ni5q5kBQ3vD5NZMrJmO+xHcacnjiLswvTFgSDMWejAJRzrNBEanbl3nOpsWBow1DXNdx9gG4rywuSMDBvP33rOfX2A8/fhj+3TxRF8FavxDW0d+zFcDQMU7T+jhDpN6XIcDQn+YmjSZ4zMzDZwPXxgwYII0sh9+Yi6CccBcHpg3YZGO3eAb5c/feGI+A+L+DWauGDAsCIBxYUI8Gzfajzvw65hHHjtQX2DvKg2ODBjmF5hncF4wNiEclCHCRzj4oS2AzPNBmMM/FjB5jNSudH0Ao20eK+E+tXUZfpgBM8+3YA5N0WhfaM/u7tKDu/D+GRk0kfXLVwYME3MQKuuqVXpygc6GJyyoRKzy1FcgMbnluxx89ePoDivLSA9+XNERLpvh6arzcwwnY759G1Rx+TLUj3pLE5hengB6c+vJHnG5ur3ckx/co4HVLucdG+/pdhUu0mBWlYq8ofPFoGF2j/zyAGw2N79jUl6njue0oUPlC5jNfvEO/8DDsVMyu8PAjstz05N/+K1f39vihW91xpy2cHtHv4KVXFd1lBkwTBBQXrjvxtV9YYyJL20GzAvC8cdggbSg//FWZzl95qevbQK4eOsvwqtN2PcJZszC513aPcoSk2AsjKCv5UWutJQx2jTCSUs7TG186NexmwQ17576otSG6y/3GLeBp7vwMP8AVswgOLrjhULs2GN3BfbYxQJzkJbxEGOpeex3jC+930gT0sxSAekND/4x7+VdK+CABT5P89/05hHjFuYryAszqsgTCIyYOU98LyOXk9kO7/DfoYMeMzzNceDWH3WZ76YcOdI+nY7pCu9vA0wTWb9Sy4BBdMsMFCodrwKsXGlvZ3bn6t3xziVXblJjhq1TEFZjUuMv49zKoJpxWAdrHUhtuqTOeKozZgbMkzuxS229E/eZW2ek3Wcu/lL/XeHPIpwQGcTkHRN03v2HZIgrP+FoZmbAAp0/3HEH5haSTsxEgolikUcwy+Y0YLHh2jUtbms2z4x31BHsloIpzIz4gydOK8uleBS/KeGAVhNo0oFWGWjHYypfXmsgg7IBQI/DgTgMisOAINytgUOgUAeKA6yNGmlzHHiH0gW4h79Ll4hw/0FaCYcMoVENB0zNhAO+0HwGNaU4PIs04FAjzBDv0aNEL7xg9kFUty4RNJwhbdBkhwOpUEYhJAgIAoKAICAICAKCQDgjgOs/MOeD4i0o9sD8Dte54B2amYX8jwAUUn3wgb7/DAqlMCfGfYlQuAQlU47X6ECLNhRYBUN5YK4M5WmOGhz9j1Joheg3BgzZRuODxjlo0OH7dKB5Dupk9+3T2vlQaaAeky+oxaWAKBz8oF0OKr7RuKFFCKpif/xRay/DO9S7erozyhP0UJsKrWpQSQ7NeUzQ9gKtLLgHA+o8oZkMZmDAZs3SWpZwRxZfvov4oaITKljBPEK7DMKDildX2po4HnkKAoJA4BCA1swmTZwv5QxcjBKyICAICAKRiQDumoIWRGiHBRMArXbQNIyrazAnihR6/32id9/NuNziMnEwVNB0jQuNMRfFVUW4584V4f7MYCEw7NjgEDIhoPbBjH+GkfHD0/OPz4A5iiCyP5bzxAFHyBeDcGiQ7SG/jW1UnBVjM0cRxL/+0v7uvtvmhg8s+iI/6k4EkZVCQGMPx42zbNimhYw2a67DeTSWiYX8M9J77Jj2wxr6oA2Nw8ABfRAO9bKZ56dvWHsOw9e4xF144Ch1JjzKUdqjlGNq6oC0e6kvqakv4lbqi9SB4K0DBoNlohgTL+aXV4gSgiCah5vpcScUftgRw91MEE9E9fB0VxR2qfiSUnD2uJuJd774slsdS+r+464tiDa2bKnv2sL2LO4lwC4dX8aHEHHvFHbkQLj5HD/eNeOdPdy7w3djYMcPxGnUX/JfEBAEBAFBQBAQBAQBQUAQEAQEAXsEYvbv329vks6vkiV1ALhkFjRmjN4i5UtCtann/2B2INPKTA9cM0Pk2adnW2yBbtqkGSWc2erfX7sHY2amb74xfxEdP05Utqy+DT5rVm0HsURHSp0I4g2uzTEQ+RYE3CIgdcYtNGIhCIQtAtLuw7ZoJWOCgCAQUQiYea6YUtgG8hNBJhjBYYcLN9i3aaMVWCQlafngJUu0sgswNMzIuIp69Wq9M/Xnn0Tz5hEtWkTUtKlm5nDLeHrozTeJPv9c717h9nek7YsvPIeIW+ORJ8g983k1V4cJzbtonkPU4XlzI/aCACOAXVbUQSFBwFcEpM74ilTwupMyDN6ykZQJAhmJgPQFGYl2YOJCGZp5rnSyM7ZEYodr8WIiMEg4IAgRxLZttf2LLxI98YTW0lKsmFZ0gYQwgalhxgpPiAXiUD2YnNde05oLGzfWrlOzk8bhm59gtsB0QalGoUJ6p81sj/f27W0m0NAILTPQyAjCE+KTN9+sFY5A6Qh2vrDj99572o38FwQEAUFAEBAEBAFBQBAQBAQBQcAVAmlmwMBQQQMLdqugEhMM0113adWk3brpqL78Uj8HDiTq0IFo6FAi7GqBsmTRT/yHhkKc+QJzBFX20ISIs15QB9+qlWbs6tTR7uPjbf7S+rZ8ORGHgzNhjoQzZ9CA2LOnVpkPphCilKBRo9SD5s4lmjmTaPRoou++0zt6r76q7eS/ICAICAKCgCAgCAgCgoAgIAgIAi4RYIUchqUh4AQhJ8+/119nH7YntATiQroffnC+yXzNGq1lkF1v326xrF2rv/iG7tdeY1uL5a239O3rp0/bzJKSLBaOFzete0tjs2ba75Ilrt1Wr67toX3RHBZrQTx61BZ3crLFMnWqvbs33rDPE8KBdkhzWJ7fDZAt8hMMfK8DaJ+Cl2CQmjogdSb064uUYeiXYWrarLiV8nZXB6QvCP26ocvQxl9E4dUwpCglE6he8elXwm7XvfcS/fGH+3sA8ubVonx799oUbkDjIM6K4eJmfxLED7/6Sl9q16uXLWTs1L31lt6pw90O0Ma4dq3N3vEN95vhXgNcxJw6ijJqUup8iOvIRkDkvyO7/NOSe6kzaUEtuPxIGQZXeUhqBIHMQkD6gsxC3n/x6jK0Tf4N4brAE86DQVTR0yVs585pRsas7RA3efuT+apeneipp/TlgWCAcNmzO0JaPTFf8LdlS1qYL3cxBtb8xIksxoV9CQazaBzWc6Bly3Ia6vnzOZi6/vz889zGbex5lKX53bVr3003bMhmnBMsRIcOxfjuKZUuX3ihAL3/vk57Kr1GpHOpM1AiFJ51Bv3fd9/lNC6dL0ANGhQzlBwVpf/7vwK0fbux4hUGtGJFDtXfbdsWHvkJgyIJyixIHxe+fVxQVrgAJUr68wABG+BgM4QBC3AefA6+Xz8yJuBE+fMTTZhAdOKEz15D3uGlS1G0YEEcnTnjXOT//htrKFCJ8ymPGzdmNZjpHMqt+d0nzx4c7diRlX78MQcdPmw6HOjBvTer33/PRp07FzbOF2ptLykpUYZymBzG3XQ67d78iz2R1JnwrDPnzkVT9+6FqG/fgorhql//inE/4xX69dfs1LhxUfryy1xBWf1nzsxNL79sdN4+0Ntvx6v+Dn6EBAF3CEgfF559nLvyDkdz6c9Dt1QDt90QhJiMGqV3rHDHGHbkHOnTT7X6/A0bHG3kO9AIPP74BXr00UuG8hVDJaYfCCubq1dnt94fFxNjMZivQ5ReLZp+SJoE4ScEpM6kDcjRo/PRDz/koGnTThi7X5etgTz77Fl69dX8VKJEitUsmF527ow1rjfxvqOFXbytW7MaYu+JNGdOLnrppTMUF+effiWY8JC0hD8C0seFfxmnN4fSn6cXwczzH1EMGM5qgQlzRydPEi1d6s42cswhkvjFF7lo374YKl06hTp1ukBYJfdEM2bkVpotO3c21GHeoJUrc9CGDdkNMa4zyuTy5ShDu2RutYOGnakHHrhMrVtfooIFr9GuXbFKtHHYsDOUnBxlaMCMJww+CxbkMsLIRnXqXDHulbtkXIidrMICg/W//+Wh337LruKtVi3RWNE/b+xuXqPZs3MZGjeNuwEMeu65BCpZMkWl4ZNPcqu4WrW6pOwg7jhvXi766afslC/fdbr//ivGFQQXKXt2LaP7+uv5qFatRIIY0/ff5zCuHkhWaahRI1H5l382BKTOhEad+fPPbDR/fi5DAuCUHfOFksRZ3ZEjT9sK1XjbuDEbffttHK1bl41uuy2ZHnroMj3yiGbaYLdwYRw1aXKZ3n03ryFREE1ff32UsPt0991XVbtF2+rc+aJqy//9F6t219auzW7chZJMDRteoWbNdFtEpJ76B4hIf/qp3s2CWOg991w1+g5DTtwFffVVnHE1yDWVx3vvLW706TkMbbq2eLy1a6RjypS8xo6g3i2vW/eKIbp+XolsHj2ahQYMMOTlb9D48fGUK5eF+vSxmY0Zk4/uuOOqwslTnt3hx/0PxyHP4EBA+rjQ6OOCo7ZkTCqkPycK5f7cWR4tY+qNxJJJCOzfH6POWYH54N+BAzY+fPnynNSrV0HKkcNi3ON20Ti3F009ehRS4nuekgzxpXXrsts5wSr0119r0cbExCgj3EI0aVJeqlAhybiy4CpNn55HhZ2UFGVcPZBFrVZfuRJlxBml3h99tKgyh3jU11/nMiZrRdT9cjgn2LlzIRV2/fqX1WQHTFeXLoWUvVnBifl9xYqchiKYbCqNhw/HGBO4IkpM6f77E40JYYrBnOenZ55JIIgrgubNi6OuXQsZ4pk5qV69K4pJbNeuMEFkM5JI6kz41JlNm/QOknnny11dRptu1aqI2k1q1uyycX9ilBJbxMIHaM+eWMKiRvv2hY1Fi+tGu05WyoXQbnr2LKiYMzBiWbNaCH1M8+ZFDDHj7Abjddm4dsSi2hqLCHrrH8xpNLdpsznesXgza1YuatfuIhUrlqLa7Zw59mKI3tr18OEFVL6wQATma/LkvMb5uPyGoiqiiRPj6exZPWyiD5k6NS+BCbtwQZth0WratDzGTr73PLvDzzFP8h14BKSPC58+LvC1JXhikP7c+zwtmPtz28w7eOqUpCSACAwcmOAy9KJFDU0pBmGF+/PPjynxHXxj5feBB4qrnSJvu2Bw747AiEEkcOHCI1SlinETtkFY/cYE5tgx1+e+mje/ZDBsxrakQffdl2hMqgqrFfnq1a8a/k6qnSpMskDVql1VE7p//81q3Dl3kfLkua4mixMmnFTMpHJk+jdhQrwhjmhRK/bYNQMhPZhwLlyYkx57TK+YgzGbO/eomjD27Hne2AUoRWBSy5e3rXibgg3LV6kzuljDoc7s3JnVuFg+mfLm9S6S179/gtrFmTz5hPXexipV8tBrr+Wjhx+2iS6OGHHGOFN23q7uoz9ZsuSwcd+ijqdfv4IGQ3TNWPDQbQmOsbv+0kv5qWPHi2oxxVP/gDjAAEIEcfz4U3ZxmT9wjvTSpWiD2dPtFzvs/fol0N69MXTTTTbRSk/tGjt09eol0rPP6jbeqNFlKlxY9xGIC+dImza9RNjhx07bqVNZjF10bYYnxB1r1kxUSkA85ZnT7Qo/tpNnxiAgfZzGORz6uIypMcERi/TnuhxCtT8XBiw42lGGpQLnIapXtxehw67U1q16BQyrvDhDARFA7PRAfC9LFgsdPZq+qvL779nVpISZL2T49tuTDNElrQkFK8eOxJMomEPEEIQOBwxYdLTFuBw7H23alM2YmOHKAM3UgZmrWFE59fgPolFdu15QIovsEKv1d955Ve3kMQOGiSZW60F4QkwxXDTFcb69PaXOaITCoc4UKZJitKFcapcX5yLdERgWMBZdulywMl9wC2YJDBjEgpkee8xZFBAMCjNfcLdoUU7lHCLBTKdO6V2jHTtiyVv/wH68PVnLKXbUEGZCgmacsIOOM25Mntp1hw4XjOtI4o2FoSKGUpLLipnjcMBYgfFC/qBFEiLXEJ9eujSnMvv++5zKD3b9vOWZ0+IKP7aTZ8YgIH2cxjkc+riMqTHBEYv057ocQrU/d571Bke9klQECIGqVa9SpUqaWeEowBQxAwbxmbFj81Hv3ufVWQeI24BBSwuxKB/8nj4dbawWe191N8djXqXH+RQmTK4eeaQolSmToiZVWG3/+efsSosiu/H0xLUImFzi3JcjIY2nT9siM6cBbrNlcz9pdQwrXL6lzpASbQ2HOoNzXKC//sqqFhvc1dGzZ3UbcKz/OXLoNnPmTBbrLpqZ0eLw8ue3tS20N6bKla/yq3pCtBeKd9LSP9gFZHwcP57Fyhg2b17Uzvrtt/MaO+RnrcykY77M7bp//3OGWHOS2gmfODGvoZgkn3HO4JQ6H4qBfty4eGNBKotaqMHuVfnyycYZsII0YoRW/DN9+nFVXzgB7vLM9q7wYzt5ZgwC0seFTx+XMTUmOGKR/lyXQ6j253IGLDjaUdCkYuXKnGqHCYozMDnCLlSePN6ZjkKFrqndKPP5jPXrbavkODSP8x844M4ERmrJkpxWVfFs7u2Jg6cQMxo+/Iw664FdqTp17Hf1OAycCXEkMHNwv2qVvUp6nHdbuzabccDfdViO4ci3RkDqTOjUGZxpggjioEEFXIr+QjEEdnZuu00v0kAs0EzYcQbhDKevhPaGnSOcO3v66fPWHxRjoB1CfMTX/gF9hjuCshDQd98dMXa/DhoKeg6q5zvvaDFmVqrhzj+bAwOcU4Wiki1bDqg+BkpAsKAEMWz0PVikwsIPcKpbV/cXr7yiVeTXrp2oGD1veeb45Bn8CEgfFzp9XPDXJv+lUPpz71gGc38uDJj38osoF+XLJxmTlmy0Zk12dW4C9+5s3uxd9XODBleUwgwo1oBIDkR4zEo5HnlEn8mAIg6EhxX4wYMTjJ22gqm+fLl0ab2KD21nEJX6+eccxgq0TbQJBQaNhaCPPsqtzmeoD9O/rl3PK1XcI0fmN0QKY9XZMlwEDWItbybn8uoBAakzoVNnIBr31lsn1W5RixZFlcKIzZuzqTb55pvx6gzkBx/kVWLHvXqdV2K+0D6IdoZ79NBGcN6SRX49VAs7q06dLhqaRHMqxgWXwUPBBxTp4HwWFO/40j9AyQcU+4BBZGU65kigfANMHkSbsSCEc1t4ImwwS/Pne7/rEHfqQJnPU08VIiz0QHkINK5iEQqi2DhzWrVqEn3zTRy1aHFJKebAmS+cE4PIYdOml61nTr3l2Zx2eQ9uBKSPC50+Lrhrkn9TJ/25ZzyDvT8XEUTP5Rd2ttFeWO5nnjmnJh4dOxZWecdqOZRT7N/vWfMfVntxNgvnsvArUyZZqYnGOQwQDsB/8cUxxXSxeBDCnjHjuFItb76AGefQPBEmU6NHn1YH+HHPDwiqoc2ikrfemqxUX0MlNg7Kb9x40C5IrGRPnHjSONCfT2k8gyVEM+fPPxa09yDZZSADP6TOaLDDpc6AeVq58rC61Bht1Ux9+54zlNecUzs4fH3E66/HG+J1encHGICB83R+zBwev+PMFAbDt97KS3xOC/3FxIknFMPirX9AOGCkoJEUF0hDQyvOazJt2ZJVaWV84QXbOS+2Q/1t3/6C0mD4yiv2avbZDT8hyvLxx8fVwlCLFkWUMRisDz44oZgtGCAdWESCNkcmqOKHmvImTWzq7r3lmf3KM/MRkD5Ol0G49HGZX6MyLgXSn7vHOtj78yiLQUh+lJr1ehc1c59VsfEdgSilrtl39xnvEiqWIe6D3Sbz+StPKYF6ePjDijbu63LnD2c1IM7DGgw9henJDneJQZVz0aIp1vMoju4h8oiqDbX67ggKQHAOxXxuxZ3bzDJHHszinZmVDk/xSp3xhE7G2/lSZ3A+CztSOANVvDgU7jink93ggmZP7cjZp7MJ6jDaLO7+c3fpurf+AYxcXJwl1Uygc2rcm6AvO3gwRl3krhURuXfrzcaXPLsLw5cydOdXzP2PgPRx/sdUQvQNAV/6Au6rpT+3xzS4+nPbXFQYMPtyyqCv4GfAMggIicZHBHzpfH0MSpxFCAJSZ0K/oKUMQ78MJQeCgD8QkL7AHyhmbhi6DG0MmBeBtMxNrMQuCAgCgoAgIAgIAoKAICAICAKCQDghIAxYOJWm5EUQEAQEAUFAEBAEBAFBQBAQBIIaAWHAgrp4JHGCgCAgCAgCgoAgIAgIAoKAIBBOCAgDFk6lKXkRBAQBQUAQEAQEAUFAEBAEBIGgRiBm//79pgR60f9tcimv6UMAh/GEBIHUICB1JjVoiVsgIHUm9OuBlGHol6HkQBDwBwLSF/gDxcwNw8xz2WlBrDa7VeamLEJi39B+vqFS3KYJJUKyLdlMBwK4JkLqTDoAjECvUmdCv9ClDEO/DCUHgoA/EJC+wB8oZm4YjmUoIoiZWx4SuyAgCAgCgoAgIAgIAoKAICAIRBACwoBFUGFLVgUBQUAQEAQEAUFAEBAEBAFBIHMREAYsc/GX2AUBQUAQEAQEAUFAEBAEBAFBIIIQCHkGLG/W3FS5QHnKniVbBBWbZFUQEAQEAUFAEBAEBAFBQBAQBEIRgZi0Jrp64Sr0br2XnLynXL9Gf5z4m/r//CrhPRAUHRVNE+v8H1VNuJXiYnNYozhw8SgNXv067Tl/UJl1KN+UBt35BE3e/Cl9uv0bqzt/vTQuXYcqxJehSZtn+itICUcQEAQEAUFAEBAEBAFBQBAQBMIYgTTvgEXf0Id5OeUKbTy+zWC6/qH/zu2nFEsK3VO4Ms1sMD4gsCHezxtNoHuL3kl4X3d0My3as4pOXjlDJXMVofceGGWNlzW9c1qtFn54yRmTnV6pOYAqJ5T3Q2gShCAgCAgCgoAgIAgIAoKAICAIRAICad4BY3D+Pr2Leq8axZ+UNUssrWw5g8rFl1JigYnXrlrt/PEysc4wujlvKdp/4TC1WtzfLsjZjd9SdoPv6kZvbvrIzk4+BAFBQBAQBAQBQUAQEAQEAUFAEMhsBNK8A+Yu4UnXkunc1QsUZfzFRGexOnu6cnuDMfuEfm83j9a3+5JWt/6Metzexmo//+HJ9L8HXqZFzf6n3HzS4HWrnfnlzoK30XXjDq1eP4w0G6v3QYb44eFLx53MK+W/hVa0/FjF+2vrL9TOldmRt7Rhp2tp8+n0liH2iPQjrGUtPlRBVC5QgX587FNzcPLugMD7779P7dq1o+PHnctmzJgx1KNHD0pOTnbwJZ+CgEZg9uzZ1KFDBzp58qQdJNu3b6e2bdvSmTNn7MzlI7QRkP4itMtPUu8bAlLPfcNJXIU2AlLP3ZdfuhkwKL8okjOBisUVUsowXri7JxU2vsEIXUy+rGLGWazut7U2dsSyKnHFfcbuVTbjvVeldlQxX1nlpmCO/HR3oduV3+RrKZQlysa8cfLzZI2jHDHZ6Njlk3Qy0XnShTibL+rjtPtVr0QNiomKoX/O7DaYwhjC2a2uFVv4nLaicQWpQPZ4qlPsbkLakPbtp/co/4nXEumvU/9yEuXpAoEmTZrQ3LlzacCAAXa2y5cvp+HDh1O9evUoNjbWzk4+BAFGYMuWLQQmrG/fvmyknqdPn6Z58+ZRYmKinbl8hDYC0l+EdvlJ6n1DQOq5bziJq9BGQOq5+/JLNwNWqcAt9K2xa/VN06n0Uf3XqHW5RmqH6onvh1pjbV72QfXeY+WLSlyxtSE6+P3+NcrsvmLVrO7wMvK3d6j2lx2oy/fP25njo3bRu5XZoUvHnOw8GZy9ep7qf/0EdVn+PL2yfqpyWqNwVfVMTdp+PrRepa3u/E40wFAyAtp5dh8989Mr6l3+uUagVKlSNH36dDWJXrhwoXJ0/vx56t69O7Vp04Y6deqkzC5cuEDbtm2jy5c1424ODbtnW7dupYsXL5qN5T2CEAATP3/+fK853rVrl9NumVdP4iBoEJD+ImiKQhISQASkngcQXAk6aBCQeu6+KNLNgLESDiji2HFmD51LuqCUY3zT9D21M4ao2y8dRHW+fNzQTniIoD2x5+1trTtfOWOzW1NnIQst3vuT+oaYoSPxeTLsQKWGfjm80aqRcfm+X5TXwjkLqKevaYPjL3ctV35cpU1ZyD+3CHTr1o0aNWpEPXv2JDBfI0aMoKSkJJo6dSpZjLIeOnQo5cmThypVqkRxcXE0ceJEa1jPPPMMFS5cmCpXrky5c+emkSOdxU+tjuUlLBGoWbMmdezYkZ588kk6ceKEyzzOmjWLqlatSuXKlaOCBQsqsVdh2F1CFfSG0l8EfRFJAv2AgNRzP4AoQQQ9AlLPXRdRuhkwKOF4etVI9eu0/Dmq/9WTtObIH0pUsPOtzVWsEE+c02SiOvcF1fVPVWpLxQ2NhY6UmJLkaGT3/Zuh8RBU3AjPHdUvWYsK3WCu2M3+i0f4lZKuJxtsnoWiDFX2IF/TBrc7DBFGobQhEGVorJw2bZo6B9a+fXuaPHkyzZgxgxISEmjOnDk0btw4tbtx6dIlgszwoEGDaM2aNbR7926aMmUKLVu2jK5du0YffvghjR492uV5srSlTHyFCgKTJk1SSe3Xr59Tkjdt2kSdO3empk2bqjNhS5YsIfxwxlAo9BCQ/iL0ykxSnHoEpJ6nHjPxEXoISD13XWbpZsBcBTvvv6XKGGe6QNMeelUxOv+e3UtT//qcOi4bTP/b+oWyiyZbEix0XZm5+3c5JZGwCxafLS/lz57XyRkuZB5772BaaIhDmum6xX24vqYN4UHBiFDaEShZsqRioDAx7t27N0E2GLRgwQIqW7YsFShQgNavX08VKlRQO12LFi2i/PnzKzdjx46lzz77TPm5fv06FSrknglXHuRf2CEAZn3mzJnqPCHOfplp8eLF6nPYsGEUHx9PjRs3phYtWii3ZnfyHjoISH8ROmUlKU07AlLP046d+AwdBKSeO5eVjftxtkuzycOl71d+oWwDd3AVMhRsQFTx8WVD6OO/F6hzU7WK3KncmDUl+hLhjwd/V2F+0mCck/NRNfTK+J8ntjvZuTJIT9pYDNGVshBXcYmZRoCZLkyOmfbu3avEyvr06UP8Q2MFo4XJ9OrVq5WSji5dulCxYsXUuTERLWP0IuuJegNRRJwfPHLEtrN97NgxqlKlihJfZUSqV6+udlD5W56hh4D0F6FXZpLi1CMg9Tz1mImP0ENA6rl9maX7HrDb8t+s1Mcj2FhDw2ARQ2MgGC6I+c0wmC0wKrgkOSFHPhpQtYvSGNi87EN0Z8GKKiW5Dc2GqaER6yZRlYQKakfth8c+oY3HtipxwhpFqqh7xyBi+JLhxhdKT9r4PFr5fDfRiOp9aMz691RefYlX3NgjgEOaEC/cuHGj1WLFihUEJgzKN6B+fOnSpQQlHdgtgzxxq1atlGpyqwd5iRgEIL6K+gFmnQl1BdoScb4QZwlBYNxr167NTuQZJghIfxEmBSnZ8IiA1HOP8IhlmCAQyfU8zTtgvAOUMyaHUh8PcUMwRgUM0UCoiX9x7dtK7TvqyFt/zqDzSZeo062P0rjaQ5Qijk/++VpVH9zrxeRC7wZb2T27fv8C/XHiH0O1fBaCivm6xe9RzBcuZ+6y/AU6fuW0cs9qPDitdoHciMyXtLlL19ZTOylrdCw9WuZBuiX+Jrvg5cN3BFq3bk04w4OzXlAtDmUKDRo0UDscUDHesmVLevfddyk6OprQWEHYGROKTAQgqvrBBx/YnQNs1qyZAgPXGhw8eFCpp4fWRIgiCoUXAtJfhFd5Sm5cIyD13DUuYhpeCERyPY8yNNApPgWH5KrNbhXQki1pKN7APVx7zh/0WzwJ2fNRqTzF6O9T/6nzYWkNOK1pw24fiJk+X+Lf0H6+0vzni9twcwNRsSJFiiilGg0bNlTZQxWEsgRoRgSVKFFC3Rk2ZMgQ9f3ee+/RqFGjrBPugQMH0vjx4ykmJt0buCr8UPiH9nmjqYZCcv2aRpzrWrVqFa1du9YuXCjdALN+9OhRpSUT58IgmoidUmjLxAXfEyZMUIy7nccI+QiHOhPp/UU4lGGENLd0ZTPS63m6wIsQz+HQF0R6PXcswwxlwCKknXjNZiQzYJ7ASUlJUUwWznk5Es6DHThwQJ0Bi8RLmx0briM+8q0RAJOKs2FQ0hJJDLqr8g/3OhMJ/UW4l6Greitm9ghEQj23z7F8uUIg3PuCSKjnjmUoDJirmh5gM2HAAgxwGAbv2HDDMIuSJT8jIHXGz4BmQnBShpkAukQpCAQhAtIXBGGhpDJJjmWY5jNgqYxXnAsCgoAgIAgIAoKAICAICAKCgCAQ8QgIAxbxVUAAEAQEAUFAEBAEBAFBQBAQBASBjEJAGLCMQlriEQQEAUFAEBAEBAFBQBAQBASBiEcgat++fUoLYunSpSMeDAFAEBAEBAFBQBAQBAQBQUAQEAQEAX8jYPBc1iBFCYcViox7ESUcGYd1uMTkeHgzXPIl+QgcAlJnAodtRoUsZZhRSEs8gkBwIyB9QXCXjy+pcyxDEUH0BTVxIwgIAoKAICAICAKCgCAgCAgCgoAfEBAGzA8gShCCgCAgCAgCgoAgIAgIAoKAICAI+IKAMGC+oCRuBAFBQBAQBAQBQUAQEAQEAUFAEPADAqliwG6JL02lchfzQ7QShCAgCAgCgoAgIAgIAoKAICAICAKRh0CMtyzHZ8tNU+uNonLxpSjK+ANdt1ho+5ld1OuHkZR47aq3IHy2z5M1jsbUGkT/t+ZNuph8WflrXLoOVYgvQ5M2z/Q5HF8clslTguY2edvJqYUsdD7pIs3avpBm/POVsv+o/mtUuUB5qv1lB0q6luzkx5VB+fibqGeltvTcL+NdWYuZICAICAKCgCAgCAgCgoAgIAhEIAJeGbAvGr1FCTny0emr5+ivk/9SlqhourPgbXRb/nL01SNTqMnCnn6D7fV7h9A9hStTbLROVs6Y7PRKzQG0+eR2v8XBAUUb+QBdSblKf5/+T73HRGehfNnyqF2+vlUepwtJl2j+ruVWxlM58vHf/x58mWKisvjoWpwJAoKAICAICAKCgCAgCAgCgkAkIOCRAUvInk8xX9gRavR1dzs8fm71mbK7OW8p2nVuv51dWj+gojGjaf+Fw/T0qpF20Xat2IL6VelE7cs/ohgwO0sfP6IpVdKdPoYqzgQBQUAQEAQEAUFAEBAEBAFBIJQR8Mgl5DZEAkHJ11Oc8vjWHx/T+mN/Ue6sOa123W9rTUubT6ff282jlS1n0Oia/Qm7SqDsWbLRW3WGEhi39e2+VL/vW3xEtYreqexfqzWQ7ip4u3pf2Ow9+r9qvYywPlTflQtUoB8f+1S9Y+fq9XsH06+tv1BhrG79GT13l405hBgj4kDccLOu7Rw7exWIl3/f7F6pXBTKmd+ly1yxOWlK3RG0ps1saxqGGellWvDwO/uyYxcAABZ9SURBVBQXm4NyGDt4SEutInewVUQ+33//fWrXrh0dP37cKf9jxoyhHj16UHKyb6KdTgGIQdgjMHv2bOrQoQOdPHnSLq/bt2+ntm3b0pkzZ+zM5SO0EZD+IrTLT1LvGwJSz33DSVyFNgJSz92Xn0cGbM/5g3Q55QoVyB5PK1p+rJginG0Cfb17BfX58WX684QWD2x7SxN6unJ7yp89ryHSt8s4J3admpS+nwbf2U25n3j//1GdYtXoqnFmbO3RP+lU4lmKN8T93rzvBYo2dr7+Ns6UXUlJVG53nt1HO8/upR1n9qjvxGuJ9Nepf9U7zmM9VLIW4TjauqOb1ZksxA2GC5TFEPvLEZNNxR1zQ5SRz5MpBz78e7zCo8rVmcTzLl3PbDieahSpSueuXqDVhzeovLa8uQFNrPN/yv3mUzvUOTmcldt2eiedTIzsCWKTJk1o7ty5NGDAADs8ly9fTsOHD6d69epRbGysnZ18CAKMwJYtWwhMWN++fdlIPU+fPk3z5s2jxETdb9hZykfIIiD9RcgWnSQ8FQhIPU8FWOI0ZBGQeu6+6DwyYPDWcdkQupB8ifJmzU2PGUzGZ40mqJ2lyXWHq10tDnrAHV0U09F0YS964vuh1OibHkpBR8ub6xvuslLVhFsVs4QzY/1/epUaG/bHr5xW572K5CyolF78YzBhoMGrX6cv/1tGA35+VX2DIXvmp1eUIozbjbNnSE/teR2UWaNvuqtzXFDWgZ0pJjA/zRY9TTXntqP3t85mY6dn0bhCNPye3uoHJu7j+mMJIoigz3Z86+S+vsH8lcxVhA5cOKLOvw0y0trg625ql/C+YndTMSO8l3+bophJMJu9V40ymMl9TuFEkkGpUqVo+vTpahK9cOFClfXz589T9+7dqU2bNtSpUydlduHCBdq2bRtdvqwVsJgxwu7Z1q1b6eLFi2ZjeY8gBMDEz58/32uOd+3a5bRb5tWTOAgaBKS/CJqikIQEEAGp5wEEV4IOGgSknrsvCq8M2KGLx+jBBV1pyC/j6JfDGw0NgZcoa5ZYJVb33aMfUKEc+RXjkzU6Vu32gKkCYQes3ZKBVP+rJw1GLInundeeHvrqCcodG0cPlqhJg+/qRnExOZRbM+OkDNz8q1u8urI5fvkUdb61ufp1KN+Uzl49rxRl3FGwotXnscsnCe5AYMbcEUQWm5d9SP2wY1epwC0E55/vWETz/lvq5I3TMP3vL6120IzIu3X33hCptFrKi0KgW7du1KhRI+rZsyeB+RoxYgQlJSXR1KlTDbwtNHToUMqTJw9VqlSJ4uLiaOLEiVbknnnmGSpcuDBVrlyZcufOTSNH2p/ZszqUl7BFoGbNmtSxY0d68skn6cSJEy7zOWvWLKpatSqVK1eOChYsqMRehWF3CVXQG0p/EfRFJAn0AwJSz/0AogQR9AhIPXddRB6VcEA0ELtThy8dp58OrVc/BHNTnuL0br2RivnqfnsbJYYHczA9ZoI/JjBdL1Xvq85GsRlUvqeGEC8Iij/6V+3s5LWcYb7t1E5lfuIGI+jkyMHgyKUThPNsoBTLNfrP2K066pAPs5fCORPU555zB8zGCgMwb6XlnjQ7XPgDClamTZtGWA1p3749LVmyhBYvXkwJCQlqZ2zcuHFqd6Nx48aEiXSvXr2oRo0aVKRIEZoyZQotW7aM6tevTzNmzFA7ZxBHK1SoEAcvzwhAYNKkSfTtt99Sv379aM6cOXY53rRpE3Xu3JmGDRtGP/30E61bt06dD8MZw7Fjx9q5lY/gR0D6i+AvI0lh+hGQep5+DCWE4EdA6rnrMvLIgE2p+5JSCz9w9Vi1+8VB7D1/SInWzX94sqGSviItMFS1g/JkzcVO1LNivrJUPFdh+uPEPzTWUJwBJYfYRcMPDN34+55TYoUoHF/ojLHTBVq6b7X1ji6zv6OXTxB24kCuFIeY3fI7NDz+eOh3/vT6PHXjPFcBQzU/mY525byxm8c7YV4DikAHJUuWpA8//FAxUL179ybIBoMWLFhAZcuWpQIFCtD69eupQoUKaqdr0aJF9Pzzzys3mEQfO3ZM+bl+/bpRl3yrM8qz/AsLBMCsz5w5k1q2bEmtW7em4sX1ggwyB2YeBAYMO6hg5Fu0aKHOHgoDpqAJuX/SX4RckUmC04CA1PM0gCZeQg4BqefOReZRBHH5/l+VjyGGIg2IHZrpwRI11CcYDpxxwm4WdoDMNPH+YfTavYOU8g3spkFhB5g53K11Nuk8lc1TUjnne78gigaKvREXiw5CsQbor5M71BNMH1Tf829UjX40s8E4KpWrqLIP5D/eYWtTrrFdNA1K3au+1xz9Qz2vkzAJdgDd+GCmC5Njpr179yqxsj59+hD/0FjBaMXHx9Pq1auVko4uXbpQsWLF1LkxES1j9CLriXoDUUScHzxy5Ig182DOq1SpopgvNqxevTrt3r2bP+UZgghIfxGChSZJTjUCUs9TDZl4CEEEpJ7bF5rHHbDFe3+iZwxRP+xirWgxg7YamgghngdNiBXylVFnqz43FFXgvNe6I38qlfKzGr6hGCwoq4D2ROx0rTiwhobd00sxaLhbC9oOu1ZsaRVHhDZE0OUbWhChkn72v98Z/tYq8/L5bqIR1fvQmPXv0YA7uhLEALH79rWhLr564Sp0q7HTBo2N/5zZrS5SVp4C9O9zI11PVWpPOOuFdK458gc9apwhg/IN3Cl2OvGcijnZOBeG825jDDfTt81T6QtQkkI+WIglXrt2jTZu3GjNy4oVKwhMGJRvQP340qVLCUo6sFsGeeJWrVop1eRWD/ISMQhMnjyZUD/ArDOhrkBbIs4X4iwhCIx77dq12Yk8wwQB6S/CpCAlGx4RkHruER6xDBMEIrmee9wBS7qeTI8sfMpQrvEfZTM0Gd5TuDI1K/OAYr6g+OKJFUMV04N68PyvE9QOFxgz3IkFxghnwl5dP5WgBn7OziVql2zwnU8qjYPx2XLTwj0/qCrUsJSeJIHpwq4XNCb2MM6WgbYaZ7ogVvhomQfpFoPx675ymAq3lHHWCufAEA9EIp/9+TXlns+V8VMZuvgHphHkzZ2jG/jrsfJFpUa/gZHukcbu2x0Fb1VKODosHazCxD9z3lqUrW81lxdnBCBOhjM8OOsF1eI4A9agQQO1wwEV4xA5e/fddyk6OlqdIUMI2BkTikwEIKr6wQcf2N0r16xZMwUGrjU4ePCgUk8PrYkQRRQKLwSkvwiv8pTcuEZA6rlrXMQ0vBCI5HoeZYj9Kbk/nKmpNruVx5Itk6cEJRhnnzYbooRgzlwRRBUrFShPBw017awR0ewOauSPGdoJ3d2NhYubSxhq3g9fPG6NA5oWQebwoDnxlvjStNkQS2RmyhxPRryDiYRikC0n/3WZBqQxr+HmiKGMhMUpka4N7ecrzX8ZkcZgiwOiYlCsAaUaDRs2VMlDFYSyBGhGBJUoUULdGTZkyBD1/d5779GoUaOsE+6BAwfS+PHjKSbG4wau8hsu/9A+WUQ3XPLkaz5wrmvVqlW0dq3eEWd/ULoBZv3o0aNKSybuBINoInZKoS0TF3xPmDBBMe7sJ5Ke4VBnIr2/CIcyjKQ2l9a8Rno9TytukeQvHPqCSK/njmWYKgYskip7IPMayQyYJ1xTUlIUk4VzXo6E82AHDhxQZ8Ai8dJmx4briI98awTApOJsGDRkRhKD7qr8w73OREJ/Ee5l6Kreipk9ApFQz+1zLF+uEAj3viAS6rljGQoD5qqmB9hMGLAAAxyGwTs23DDMomTJzwhInfEzoJkQnJRhJoAuUQoCQYiA9AVBWCipTJJjGXo8A5bKsMW5ICAICAKCgCAgCAgCgoAgIAgIAoKABwSEAfMAjlgJAoKAICAICAKCgCAgCAgCgoAg4E8EhAHzJ5oSliAgCAgCgoAgIAgIAoKAICAICAIeEIjat2+f0oJYunRpD87EShAQBAQBQUAQEAQEAUFAEBAEBAFBIC0IGDyX1Zso4bBCkXEvooQj47AOl5gcD2+GS74kH4FDQOpM4LDNqJClDDMKaYlHEAhuBKQvCO7y8SV1jmUoIoi+oCZuBAFBQBAQBAQBQUAQEAQEAUFAEPADAsKA+QFECUIQEAQEAUFAEBAEBAFBQBAQBAQBXxAQBswXlMSNICAICAKCgCAgCAgCgoAgIAgIAn5AQBgwP4AoQQgCgoAgIAgIAoKAICAICAKCgCDgCwIxvjjyt5slj06jhBz5PAa78fg2enrVSLduOlVoRgPu6EqT/vyEZu341qW7bre1ot6VO9D4jdNp3n9LXboRQ0FAEBAEBAFBQBAQBAQBQUAQEAQyCoFMYcBWHlxH5fKWsubx7kK30zXLNfrzxHar2c+H1lvfXb5ERWljfrpwFEXaDTSPCAkCgoAgIAgIAoKAICAICAKCgCCQ2QhkCgM2YdOHdvn+vd08upR8xeOOl50H+RAEBAFBQBAQBAQBQUAQEAQEAUEgBBEI2jNgpXIXo88bvUm/tvmC1rf7ksCkzW3yNhWLK2QHc/XCVeinVp8qN3h2vrW5nb3jR6dbH6UfH9Pu17SZTR88+ArljMnu6Ey+/YjA+++/T+3ataPjx487hTpmzBjq0aMHJScnO9mJQeQiIHUmcsvenPPZs2dThw4d6OTJk2Zj2r59O7Vt25bOnDljZy4fgkCoICB9XKiUlKQzPQhIPXePXtAyYDMbjKNb4kvT0Usn6PdjWygx5SqVyVOC3qwz1C43tYrcoexWH95IsdGx1L9qZ2pTrrGdG/54qlJbGlC1C+UwGK4tJ3fQ4UvH6c6CFWmOwdgJBQ6BJk2a0Ny5c2nAgAF2kSxfvpyGDx9O9erVo9jYWDs7+YhsBKTORHb5c+63bNlCYML69u3LRup5+vRpmjdvHiUmJtqZy4cgECoISB8XKiUl6UwPAlLP3aMXlAzYHQVvpbjYHLTjzB5qtbg/9f1xNNX/6kmyGH8lchW2y83llCvUZGFPGrR6LD2+bIiyA6PlSNHGObAnK7ZSxs0WPU3dV75IrY2wt57aSUVyJlCjUvc5epFvPyFQqlQpmj59uppILVy4UIV6/vx56t69O7Vp04Y6deqkzC5cuEDbtm2jy5cvO8WM3bOtW7fSxYsXnezEIPwQkDoTfmWanhxhAWf+/Pleg9i1a5fTbplXT+JAEMgEBKSPywTQJcoMR0DquXvIg5IBgzKOGnPbUrcVw6hkriL0aJkHaXj13mSxEGWJymKXm82G2+uwMGjP+YN0xNgxi8+Wx84NPkrnLk4x0VkIDBuYLYgq4nchSU/oaxe7y8mPGPgPgW7dulGjRo2oZ8+eBOZrxIgRlJSURFOnTjXK1UJDhw6lPHnyUKVKlSguLo4mTpxojfyZZ56hwoULU+XKlSl37tw0cqR77ZhWT/IS8ghInQn5IvRLBmrWrEkdO3akJ598kk6cOOEyzFmzZlHVqlWpXLlyVLBgQSXyLIs1LqESwyBCQPq4ICoMSUrAEJB67hraoGTAkNQ+lTsaZ7tm0YJHptCI6n2oSen7CbtYjvSTg7bEM1fPKSeFcuS3c3prvrLqO2dMDiWmCFFF/GoVvVOZF4+z31mz8ywf6UYAmiinTZumzoG1b9+eJk+eTDNmzKCEhASaM2cOjRs3Tq1wX7p0iSAzPGjQIFqzZg3t3r2bpkyZQsuWLaNr167Rhx9+SKNHj3Z5nizdiZQAggoBqTNBVRyZmphJkyap+Pv16+eUjk2bNlHnzp2padOm6kzYkiVLCD+cLxUSBIIZAenjgrl0JG3+QkDquWskM0ULouuk2Ezrl6xFXSu2oOTrKbRo74+05sgftPrQBlre4iPjnJfnJOeOjVMBHb9y2hag8Xbixvehi8do8C/j7Ozwce7qBSczMfAvAiVLllQMFEQPe/fuTZANBi1YsIDKli1LBQoUoPXr11OFChXUTteiRYvo+eefV27Gjh1Lx44dU36uX79OaNBC4Y+A1JnwL2NfcoiFmpkzZ1LLli2pdevWVLx4cau3xYsXq/dhw4ap3fPGjRtTixYt1LlT9BtCgkAwIyB9XDCXjqTNXwhIPXdGMih3wOqXvFeldOpfn9PLv02h7/f/SgWNi5tzxGQzJt72mahfSruFaZ6scVTcEFmEmKEj4awXqFiuQnTg4hHadW6/+jW9qR592nA8PVr2QUcv8h0ABJjpwgSJae/evUq0qE+fPsQ/NFYwWvHx8bR69WqlpKNLly5UrFgxdW5MxIsYvfB/Sp0J/zL2JYfoMyCKiAWcI0eOWL1gYaZKlSqK+WLD6tWrq91z/panIBDMCEgfF8ylI2nzFwJSz+2R9LydZO82w75wUfNDxi5Yx/JN6cTl01Q0riB1u621ij86yp5nrFaoEo2pNZA2Hd9GPQ3lGxBT/OSfr53SmnjtqtKmCLX1Sx6dRgt2LTcYttzU8ub6aqdt9r/fOfkRg4xBAIc0IV64ceNGa4QrVqwgMGFQvgEV1EuXLiUo6cBuGeSJW7VqpdRTWz3IS0QhIHUmoorbmlmILqNvwEINE/oJaEvE2VKcIwVh0aZ27drsRJ6CQMghIH1cyBWZJDgNCERyPbfnZtIAnr+8QMMhE3a8/jr1LxXIno9erfUs9a3yOB2/ckqZRVEU3VfsblIaOQwPO8/uo4alatPQak8Z7uNp0Z5V9NHfWlsWhwklD6DBq8fRhuNbDcYrFz1R8TF67OYGdOrKWRq5brKxaybqjBVImfAPIkU4x4GzXlAvjQP1DRo0UKvcUDMNsaN3332XoqOjCY0VhJ0xochFQOpMZJY9xJQ/+OADuzOgzZo1U2DgSouDBw8q9fTQmghRRCFBIFQRkD4uVEtO0p0aBCK5ngfFDlj1OW2cygsaELHbVTWhglJH78gg/UIbadaOb5U/XKRcIV8ZxaClXL9mDQuMGDNjMMQuWO9Vo5Q2xNvz32JoTTxA55MuWd3LS+ARABPlSLik+b///iNoO8SvRIkS9MYbb6j7weAWmhJHjRpF/fv3V14HDhyoGDTHcOQ7PBGQOhOe5ZrWXDVv3lxdXYGFGtSNihUrqvNeEE1855131PlR9BE4EyYkCIQCAtLHhUIpSRrTi4DUc3sEo4zdIbU9BKUG1Wbre7LsnciXvxHY0H6+Ur3u73BDPbyUlBS1so1zXo6E82AHDhxQZ8Ai8dJmtE/eyXXEJpK/pc64L/1IqzNoHzgbVqhQIYqJCYq1RfeF46NNpJWhj7BElDPp4yKquN1mNtz7gkio545lKAyY2+oeOAthwAKHbbiG7NhwwzWfki//ISB1xn9YZlZIUoaZhbzEKwgEFwLSFwRXeaQlNY5l6CwPlpZQxY8gIAgIAoKAICAICAKCgCAgCAgCgoBXBIQB8wqROBAEBAFBQBAQBAQBQUAQEAQEAUHAPwgIA+YfHCUUQUAQEAQEAUFAEBAEBAFBQBAQBLwiELVv3z6lhKN06dJeHYsDQUAQEAQEAUFAEBAEBAFBQBAQBASB1CFg8FxWD1YlHPv377fesWS1lZeAICBYBwTWsA5U6kxYF29AMid1JiCwZmigUoYZCrdEJggELQLSFwRt0ficMMcyFBFEn6ETh4KAICAICAKCgCAgCAgCgoAgIAikDwFhwNKHn/gWBAQBQUAQEAQEAUFAEBAEBAFBwGcEhAHzGSpxKAgIAoKAICAICAKCgCAgCAgCgkD6EPh/6Tcclm4udr4AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "In this tutorial notebook, we have showcased experiments ranging from general inquiries to systematic questions and chain of thought prompting techniques and observed Hallucination and correctness metrics.\n",
    "\n",
    "However, it should be noted that the outputs from GPT-4V can be somewhat inconsistent, and the levels of hallucination are slightly elevated. Therefore, repeating the same experiment could result in different answers, particularly with generalized questions."
   ]
  }
 ],
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   "language": "python",
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  "language_info": {
   "name": "python"
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   "interpreter": {
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